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Novikov, D. V., A. S. Stankevich, E. G. Silkis, A. M. Torubarov, and G. A. Perepelkin. "THE MORS-4 SPECTRA RECORDING SYSTEM WITH THE RASPBERRY PI 3 MODEL B MICROCOMPUTER." NAUCHNOE PRIBOROSTROENIE 28, no. 3 (August 29, 2018): 24–28. http://dx.doi.org/10.18358/np-28-3-i2428.
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Sedayu, Agung, Elvan Yuniarti, and Edi Sanjaya. "Rancang Bangun Home Automation Berbasis Raspberry Pi 3 Model B dengan Interface Aprlikasi Media Sosial Telegram sebagai Kendali." Al-Fiziya: Journal of Materials Science, Geophysics, Instrumentation and Theoretical Physics 1, no. 2 (April 2, 2019): 42–47. http://dx.doi.org/10.15408/fiziya.v1i2.9254.
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Telah berkambangnya tekonologi home automation pada zaman ini dan salah satunya dengan pemanfaatan smartphone sebagai salah satu pengendalinya. Pada penelitian ini memanfaatkan sebuah aplikasi media sosial telegram dan sebuah single board computer sebagai kontrol pada sebuah home automation. Penelitian ini bertujuan untuk membangun sebuah home automation dengan menggunakan Raspberry Pi dan aplikasi media sosial telegram sebagai kendalinya. Dengan studi pustaka dan metode eksperimen penelitian ini berhasil merancang bangun sebuah home automation menggunakan Raspberry Pi 3 Model B dengan interface aplikasi telegram sebagai kendalinya. Dibutuhkan sebuah Bot API telegram agar Raspberry Pi dan aplikasi telegram dapat terhubung. Perangkat yang digunakan dalam penelitian ini meliputi: single board computer Raspberry Pi 3 Model B, 2 buah relay untuk 2 perangkat elektronik (lampu dan kipas), 1 buah MCB dan smartphone yang telah terinstall aplikasi telegram yang digunakan sebagai pengontrol perangkat elektronik. Hasil dari penelitian ini sudah sesuai dengan tujuannya yaitu merancang bangun sebuah home automation dengan menggunakan Raspberry Pi dan aplikasi media social telegram sebagai kendalinya mensimulasikannya pada peralatan elektronika.
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Endang Supriyadi, Maya Sofiana, and Surya Dwipangga. "Sistem Kendali Lampu Defect Dan Reject Berbasis Web Server Menggunakan Raspberrry Pi 3 Model B." Jurnal Teknik Informatika 7, no. 1 (February 2, 2021): 09–15. http://dx.doi.org/10.51998/jti.v7i1.346.
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Abstract— This research covers the design and construction of a Web-based Defect & Reject Light Control Intelligent System with Raspberry Pi with the aim of detecting the quality conditions of manufactured goods at the final inspection section of PT. Suryaraya Rubberindo Industries. This system requires several components such as a Raspberry Pi 3 Microcontroller, Relays, mini filament LED lamps and several other supporting components. The design and construction of a Web-Based Defect & Reject Light Control Intelligent System with the Raspberry Pi comes from experimental results, besides that it is also supported by several literary documents found in journals and reference books. This Intelligent Web-Based Defect & Reject Light Control System with the Raspberry Pi uses a website design as an input to turn off or turn on a lamp. Two mini filament lamps are used as output components that are made like Signal lamps. Based on the results of the experiments conducted, the Web-Based Defect & Reject Light Control Intelligent System with the Raspberry Pi can work quite well. When this system detects the condition of the production goods in a state that does not meet the specified company standards but has a low level of seriousness (defect), the system will give a signal for a yellow light with a value of 1, whereas if the level of seriousness is high (reject) then the control system will give a signal to turn on the red light the value of the number 1 stored in the database
Intisari— Penelitian ini meliputi racang dan bangun Sistem Cerdas Kendali Lampu Defect & Reject Berbasis Web denganRaspberry Pi dengan tujuan untuk mendeteksi kondisi kualitas barang produksi pada bagian final Inspection di PT. SuryarayaRubberindo Industries. Sistem ini memerlukan beberapa komponen seperti Mikrokontroller Raspberry Pi 3, Relay,Lampu berjenis LED filamen mini dan beberapa komponen pendukung lainnya. Rancang Rangun Sistem Cerdas KendaliLampu Defect & Reject Berbasis Web dengan Raspberry Pi tersebut berasal dari hasil percobaan, selain itu didukung pulaoleh beberapa literatur dokumen yang terdapat pada jurnal dan buku referensi. Sistem Cerdas Kendali Lampu Defect & RejectBerbasis Web dengan Raspberry Pi ini menggunakan rancangan website sebagai input untuk mematikan atau menyalakan sebuah lampu. Dua buah lampu filamen mini digunakan sebagai komponen output yang dibuat seperti lampu Signal.Berdasarkan hasil percobaan yang dilakukan, Sistem Cerdas ini dapat bekerja dengan cukup baik. Ketika sistem ini mendeteksikondisi barang produksi dalam keadaan tidak memenuhi standard perusahaan yang telah ditentukan akan tetapi memilikitingkat keseriusan rendah (defect) maka sistem akan memberikan sinyal untuk lampu kuning bernilai angka 1 sedangkan jika tingkat keseriusan tinggi (reject) maka sistem kendali akan memberikan sinyal untuk menyalakan lampu merah bernilai angka 1 yang tersimpan pada database.
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Dhakate, Prajwal. "NFC based Smart Attendance System using Raspberry Pi 3 Model B+." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 1830–35. http://dx.doi.org/10.22214/ijraset.2020.5293.
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Sudarsono, Joshua Fernaldy, Gede Sukadarmika, and Linawati Linawati. "Rancang Bangun Alat Ukur Kualitas Jaringan Berbasis Raspberry Pi 3 Model B." Majalah Ilmiah Teknologi Elektro 20, no. 1 (March 1, 2021): 53. http://dx.doi.org/10.24843/mite.2021.v20i01.p06.
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Internet telah menjadi kebutuhan bagi masyarakat modern dalam melakukan berbagai aktifitasnya saat ini. Pengguna Internet yang meningkat sangat pesat di setiap tahun menunjukkan bahwa kebutuhan masyarakat terhadap Internet yang semakin tinggi. Di Indonesia pertumbuhan pengguna Internet diprediksi meningkat 10,2 persen setiap tahunnya dari tahun 2018 hingga 2023. Namun demikian, pertumbuhan jumlah pelanggan sering kali tidak diikuti dengan kemampuan provider Internet untuk meningkatkan fasilitas maintanance dan untuk menjaga kualitas layanan kepada pelanggannya. Salah satu penyebabnya adalah keterbatasan perangkat yang sering kali menjadi kendala bagi teknisi untuk mealakukan maintenance sesegera mungkin. Penelitian ini bertujuan untuk dapat merancang dan membangun perangkat yang dapat digunakan sebagai alat ukur kualitas jaringan yang lebih sederhana dan ekonomis sehingga mendukung mobilitas teknisi untuk membagun maupun maintenance jaringannya. Perangkat yang dibangun pada penelitian ini berbasis Raspberry Pi 3 Model B. Perbandingan hasil pengukuran performa perangkat antara lain daya tahan baterai, ping, upload dan download rate antara perangkat yang dibangun dengan penggunaan laptop memberikan hasil yang hampir sama. Peangkat yang dibangun ini memiliki keunggulan pada sisi dimensi yang lebih kecil dan ringan serta biaya yang jauh lebih ekonomis.
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Muttaqin, Imam Wildan, and Arif Rahman. "Sistem Presensi Berbasis RFID Menggunakan Raspberry Pi 3." Buletin Ilmiah Sarjana Teknik Elektro 1, no. 1 (August 19, 2019): 27. http://dx.doi.org/10.12928/biste.v1i1.850.
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Sistem presensi mahasiswa banyak dilakukan secara konvensional. Sistem presensi otomatis yang ada pun memiliki kinerja yang lambat dan tidak mampu diperbarui secara otomatis. Sistem presensi mahasiswa berbasis RFID menggunakan Raspberry Pi 3 diharapkan mampu menangani masalah-masalah tersebut. Sistem pada penelitian ini dibangun menggunakan modul RFID jenis MIFARE RC522, Raspberry Pi 3 model B, dan RTC DS1307. Prinsip kerja sistem ini yaitu menerima masukan berupa hasil identifikasi ID pada kartu RFID, kemudian hasilnya diolah Raspberry Pi 3 sekaligus menentukan respon, dan merekap data presensi pada server. Hasil presensi berupa nama mahasiswa dan mata kuliah ditampilkan pada LCD 16x2. Sebagai hasil akhir, server mengirim data presensi yang dapat ditampilkan pada komputer klien melalui situs web. Pengujian sistem menunjukkan jarak maksimal identifikasi ID sejauh 4,5 cm dengan rata-rata waktu pembacaan selama 150,53 ms. Penambahan RTC DS1307 menjadikan sistem tidak harus terhubung dengan internet untuk update secara otomatis. Selain itu, penelitian ini juga membuktikan bahwa penghalang kayu, karet, keramik, kaca, plastik, dan akrilik, tidak mempengaruhi jarak dan waktu dalam proses identifikasi kartu RFID. Namun penghalang jenis logam dapat menghalangi proses identifikasi kartu RFID.
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Sałuch, Mateusz, Daniel Tokarski, Tomasz Grudniewski, Marta Chodyka, JerzyAntoni Nitychoruk, Paweł Woliński, Beata Jaworska, and Grzegorz Adamczewski. "Raspberry PI 3B + microcomputer as a central control unit in intelligent building automation management systems." MATEC Web of Conferences 196 (2018): 04032. http://dx.doi.org/10.1051/matecconf/201819604032.
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This article aims to show the possible savings in electricity costs in smart building installations with the use of new version of Raspberry Pi 3 model B + as the control unit in intelligent building automation systems. It presents a comparison of the consumption of electricity in two units used in the central control systems, i.e. a small Windows-based computer and a Raspberry microcomputer. The power consumption of these units was measured during the rest period and during standard operations in the intelligent installation system. The conducted measurements proved that the use of the new updated version of Raspberry Pi 3 model B + as the central control unit in intelligent building management systems is more economical and energy-saving.
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Yenni, Helda, and M. Ari Ardianto. "ALAT DIGITAL PENCETAK KUE BAWANG MENGGUNAKAN RASPBERRY PI 3 MODEL B BERBASIS ANDROID." JTT (Jurnal Teknologi Terapan) 6, no. 1 (April 30, 2020): 93. http://dx.doi.org/10.31884/jtt.v6i1.246.
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Kue bawang merupakan salah satu makanan ringan tradisional di Indonesia. Makanan berbahan dasar tepung terigu dan tapioka ini bercita rasa gurih dan garing. Saat ini, kue bawang sudah merambah bisnis kuliner. Peralatan utama dalam pembuatan kue ini disebut ampia. Ampia yang digunakan masih berupa perangkat manual dalam operasionalnya termasuk mengatur ketebalan adonan setelah digiling. Kendala yang dihadapi berupa keterbatasan tenaga manusia untuk menggerakkan alat dan keterbatasan ketelitian untuk mendapatkan ukuran ketebalan yang presisi sesuai dengan yang diinginkan. Pada industri skala besar, hal tersebut dapat berpengaruh pada produktifitas. Solusi dari permasalahan tersebut berupa penerapan teknologi pada industri kuliner dengan membuat alat digital pencetak kue bawang secara otomatis. Kontrol utama adalah Raspberry pi 3, aplikasi smartphone android untuk menjalankan dan mematikan mesin pencetak kue bawang secara otomatis. Sensor ultrasonik digunakan untuk mendeteksi adonan, alat ini dilengkapi dengan motor penggerak yaitu yang digunakan untuk menggiling, mencetak serta memotong adonan kue dan motor servo untuk mengatur ketebalan adonan. Sensor Rotary Encoder ky-040 yang berfungsi sebagai pengatur panjang pemotongan adonan kue bawang. Pada penelitian ini sistem yang dirancang mampu menghasilkan sistem kontrol menggunakan aplikasi smartphone Android, sehingga dapat menghemat tenaga dan waktu dalam proses pembuatan kue bawang serta ukuran dapat diatur sesuai dengan yang diinginkan. Kata Kunci: kue bawang, Raspberry Pi 3, otomatis, Android
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Muck, P. Y., and M. J. Homam. "Iot Based Weather Station Using Raspberry Pi 3." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 145. http://dx.doi.org/10.14419/ijet.v7i4.30.22085.
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Weather is the day-to-day state of atmosphere that is hard to predict which affects the activities of mankind and has great significance in many different domains. However, the current weather station in the market is expensive and bulky which cause inconvenience. The aim of this project is to design a weather station with real time notifications for climatology monitoring, interface it to a cloud platform and analyse weather parameters. In this project, a weather station is assembled using SparkFun Weather Shield and Weather Meter and Arduino Uno R3 to collect weather parameters. Data collected from the sensors are then stored into Google Cloud SQL using Raspberry Pi 3 Model B which acts as a gateway between them and analysis of weather data are done. A website and mobile application are developed using Google Data Studio and Android Studio respectively to display the real-time weather conditions in graphical presentation which are accessible by administrator and users. Users will receive notification regarding the weather conditions at that particular place on social media platform regularly and irregularly. Weather prediction is done in short term which allows users to get themselves prepared for their future plan in the next thirty minutes.
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Agustian, Indra, Faisal Hadi, and M. Khairul Amri Rosa. "Pre-Diagnosis Gangguan Ginjal Melalui Citra Iris Mata Menggunakan Raspberry PI Dengan Metode Convolutional Neural Network (CNN)." JURNAL AMPLIFIER : JURNAL ILMIAH BIDANG TEKNIK ELEKTRO DAN KOMPUTER 9, no. 1 (May 30, 2019): 16–25. http://dx.doi.org/10.33369/jamplifier.v9i1.15396.
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ABSTRAKPenelitian ini melakukan perancangan aplikasi pengenalan gangguan ginjal dini melalui citra digital iris mata menggunakan metode convolutional neural network (CNN) dengan antarmuka Raspberry Pi 3 model B+. Hasil akurasi terbaik yang diperoleh dengan memvariasikan banyak epoch, nilai learning rate, ukuran kernel, komposisi database, dan fungsi pooling layer adalah 94% pada saat epoch 12, 92% pada nilai 0,0001, 95% pada ukuran 3x3, 95% pada komposisi 100 train dan 50 validation, 90% menggunakan fungsi max pooling. Kata kunci: gangguan ginjal, iridology, convolutional neural network, raspberry pi.
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Utami, Pipit, Abdul Aziz Sidiq Tri Putra, Djoko Santoso, Nuryake Fajaryati, Bonita Destiana, and Mohd Erfy Ismail. "VIDEO MOVING SURVEILLANCE YANG TERINTEGRASI YOUTUBE MENGGUNAKAN RASPBERRY PI 3." Elinvo (Electronics, Informatics, and Vocational Education) 3, no. 1 (August 14, 2018): 113–23. http://dx.doi.org/10.21831/elinvo.v3i1.20797.
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Static CCTV facilities in class need to be optimized in classroom learning, especially in recording learning activities as implementation of learning in the 21st century and strategic steps to face the Industrial Revolution 4.0. Educators need to play a role in utilizing CCTV in learning. This article presents the development of YouTube Integrated Video Moving Surveillance devices using Raspberry Pi 3. The development stages consist of analysis, design, development and evaluation. The analysis shows that: (1) the limitations of CCTV motion are followed up with the addition of motorcycles; (2) limited access to video recording data is followed up by sending in real time using YouTube; and (3) controlling the system performance of the device using Raspberry Pi 3 model B. The evaluation results show that all electronic measurement parameters are in accordance with the success target and the functional test shows that the device can function to record classroom learning activities that can be monitored in real time via YouTube with good quality (average delay is 22,89s). The results of the development of this device are expected to be an alternative use of technology in learning, especially in the supervision and assessment of student learning activities.
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Ludony, Stephanie Graciela, Melisa Mulyadi, and Kumala Indriati. "Rancang Bangun Purwarupa Lengan Robot Berbantuan Raspberry Pi." Jurnal Elektro 13, no. 2 (February 17, 2021): 115–24. http://dx.doi.org/10.25170/jurnalelektro.v13i2.1979.
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Persaingan di bidang industri telah mendorong untuk dilakukannya proses otomasi dalam mengoperasikan peralatan mesin-mesin industri dan kontrol proses untuk menggantikan operator tenaga manusia. Teknologi robot merupakan bagian dari otomasi yang banyak diterapkan di industri, salah satunya adalah lengan robot. Pada proses produksi, lengan robot digunakan untuk memindahkan objek. Dibutuhkan perancangan yang benar agar lengan robot dapat bergerak sesuai dengan kriteria yang diinginkan. Oleh karena itu pada penelitian ini dilakukan perancangan dan pembuatan purwarupa lengan robot yang memiliki lima derajat kebebasan atau Degree of Freedom (DoF) dan dilengkapi dengan pencapit objek. Sebagai pengendali lengan robot digunakan mikrokomputer Raspberry Pi 3 Model B+ yang diprogram dengan perangkat lunak python. Pada lengan robot terdapat kamera untuk mendeteksi warna objek agar lengan robot dapat memindahkan dan menempatkan objek sesuai kelompok warnanya. Pengujian terhadap rancangan lengan robot menunjukkan bahwa kamera dapat mengenali warna objek dan lengan dapat memindahkan objek sesuai pada tempatnya.
Competition in the industrial sector has pushed for automation processes in operating industrial machine tools and process control to replace human labor operators. Robot technology is a part of automation that is widely applied in industry, one of which is the robot arm. In the production process, robotic arms are used to move goods. Correct design is needed so that the robot arm can move according to the desired criteria. Therefore, in this study, the design and manufacture of a robot arm prototype that has five degrees of freedom (DoF) is carried out and is equipped with a clamp. As a controller for the robot arm, a Raspberry Pi 3 Model B + microcomputer is used, which is programmed with python software. On the robot arm, there is a camera to detect the color of the object so that the robot arm can move and place object according to the color group. Testing of the robot arm design shows that the camera can recognize the color of goods and the robot arm can move object according to their place.
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Wardana, I. Nyoman Kusuma, Julian W. Gardner, and Suhaib A. Fahmy. "Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction." Sensors 21, no. 4 (February 4, 2021): 1064. http://dx.doi.org/10.3390/s21041064.
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Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively.
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Sulistyo, Meiyanto Eko, Stephanus Hanurjaya, and Muhammad Danang Prastowo. "Monitoring Print Engine Output Using Arduino and Raspberry Pi." Journal of Electrical, Electronic, Information, and Communication Technology 3, no. 1 (April 30, 2021): 6. http://dx.doi.org/10.20961/jeeict.3.1.49771.
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In the printing industry process, monitoring is necessary for quality control of the product. The making of the tool on this project serves to monitor the output of the production machine. This monitoring is done by detecting the product output from the production machine using Sensor E18 D80NK. When the sensor detects the output, the sensor sends a signal to the Arduino UNO R3 which will calculate the amount of output from the product. Arduino will send information of the number of outputs via a USB connection to a central computer that is a Raspberry Pi 3 model B. The Python program on Raspberry Pi will read input from each Arduino address and display the data in realtime. At the same time, the data will be stored as a text file. This text file contains the number of product output and the time of the output. The prototype of this tool has been successfully created and there is still much development to do.
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Dasmen, Rahmat Novrianda, and Nasrul Halim. "IMPLEMENTASI PAPAN INFORMASI DIGITAL MENGGUNAKAN RASPBERRY PI 3 PADA STIPER SRIWIGAMA PALEMBANG." Computatio : Journal of Computer Science and Information Systems 2, no. 2 (October 31, 2018): 196. http://dx.doi.org/10.24912/computatio.v2i2.2570.
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STIPER Sriwigama Palembang is one of the high school agricultural sciences located in the city of Palembang. As with other universities, in the STIPER Sriwigama Palembang environment there are lecturers, staff and students. STIPER Sriwigama Palembang in informing about the presence of lecturers is currently still using a simple manual information board to display the names of lecturers present or not with a static, unattractive and inaccurate display. The purpose of this research is to build a dynamic digital information board where information in it can be arranged according to needs, which can be managed by the information section. This research uses Raspberry Pi which will connect the LED TV with the computer part information. This research will produce an information board that contains interesting features such as the use of running images and text and provide information about the presence of lecturers who are teaching or not teaching which can be arranged through the computer information section. Because it is still in the research process, the current output will be explained limited to the display design and also explained about the configuration of the Raspberry Pi 3 model B so that it can modify the LED TV into a digital information board.
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GANESH, E. N. "Health Monitoring System using Raspberry Pi and IOT." Oriental journal of computer science and technology 12, no. 1 (March 7, 2019): 08–13. http://dx.doi.org/10.13005/ojcst12.01.03.
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Health Monitoring system using IOT describes the collection and interoperation of Patient data collected from the sensors from the hospitals through IOT Technology. The collected sensor data will support the doctor in the emergency situation for the betterment and improvement of Patient health. The hardware platform to implement the project consists of a sensor and Raspberry Pi 3 Model B equipped in a way to communicate with a doctor through the Internet and Smart Phone. This proposed idea will help doctors to know about the state of patient health and monitor anywhere in the world. In this proposed idea the sensors gather the medical information of the patient that includes patient’s heart rate, blood pressure, and pulse rate Then using the camera the patient is livelily monitored through the Raspberry kit and this information is sent to the Internet and stored in a medical server. The doctor and patient can monitor the patient data from any place of the world through the provided IP server address anytime. The emergency alert is sent to the patient if the sensor value is exceeded by the threshold data. Thus the patient's health parameters are watched lively and regular monitoring through the medical server to a doctor will help to make an effective diagnosis and almost accurate care can be given. The data collected through the IOT will help the patient to recover easily and also enhanced medical care can be given to the patients at a low cost.
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Wiguna, Esa Hayyu, and Arkhan Subari. "RANCANG BANGUN SISTEM MONITORING KETINGGIAN AIR DAN KELEMBABAN TANAH PADA PENYIRAM TANAMAN OTOMATIS DENGAN HMI (HUMAN MACHINE INTERFACE) BERBASIS RASPBERRY PI MENGGUNAKAN SOFTWARE NODE-RED." Gema Teknologi 19, no. 3 (October 31, 2017): 1. http://dx.doi.org/10.14710/gt.v19i3.21878.
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Esa Hayyu Wiguna, Arkhan Subari, in this paper explain that the monitoring system is a system used to monitor and control work processes in a plant design. This system is widely used and applied in the industrial world to find out the performance of a plant. To do the monitoring system, a lot of software can be used, which is then called the HMI (Human Machine Interface). The monitoring system with an interface in the form of HMI can be presented in various forms, such as buttons, or can also be displayed in the visualization of the plant while working. This monitoring system through an HMI interface uses supporting hardware in the form of a Raspberry Pi as a device to process the data that will be displayed on the display screen, while displaying its visualization uses an LCD touch screen. This LCD touch screen is connected to the Raspberry Pi via the LCD driver. The graphic form that will be displayed on the LCD touch screen is designed using Node-RED software. The visualization that will be displayed on the Touch Screen LCD will be adjusted to the working system of automatic plant sprinklers. This monitoring system using an HMI interface can display the plant's working system through indicators of water level and soil moisture. To test tube 2 water level measured through ultrasonic sensors through HMI has an error ratio of 1.01%, while for soil moisture measured through soil moisture sensors has an error ratio of 1.51%. Keywords: Monitoring System, Human Machine Interface (HMI), Raspberry Pi, Node-RED. ReferencesHaryanto, Heri dan Sarif Hidayat. 2012. Perancangan HMI (Human Machine Interface) Untuk Pengendalian Kecepatan Motor DC. Jurnal S1 Jurusan Elektro Fakultas Teknik Terpublikasi. Banten: Universitas Sultan Ageng Tirtayasa.Udayana, Gede Agus, I Gede Mahendra Darmawiguna, dan I Made Gede Sumarya. 2016. Pengembangan Prototipe Portal Otomatis Dengan Pendeteksian Plat Nomor Kendaraan Berbasis Raspberry Pi. Artikel Jurusan Pendidikan Teknik Informatika Terpublikasi. Bali: Universitas Pendidikan Ganesha.Man, Joseph. 2016. Raspberry Pi 3 Model B Technical Specifications. https://www.element14.com/community/docs/DOC-80899/l/raspberry-pi-3-model-b-technical-specifications. Diakses tanggal 14 Agustus 2017.Kurniawan, Halim. 2005. Aplikasi Penjawab Pesan Singkat Automatis dengan Bahasa Python. Makalah Seminar Tugas Akhir S1 Jurusan Teknik Elektro Terpublikasi. Semarang: Universitas Diponegoro.Node-RED. 2013. Node-RED; Flow-based programming for the Internet of Things. https://nodered.org/. Diakses tanggal 02 Mei 2017.Tim J, M. 2016. Developing with Node-RED. https://software.intel.com/en-us/articles/developing-with-node-red. Diakses tanggal 02 Mei 2017.
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Nguyen, An Toan, Ngoc Thien Nguyen, and Thanh Truc Nguyen. "Research of object recognition using neural network Inception-v3 model operating on Raspberry Pi B3+." Journal of Science, Quy Nhon University 15, no. 1 (February 25, 2021): 13–22. http://dx.doi.org/10.52111/qnjs.2021.15102.
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Image Classification is the most important problem in the field of computer vision. It is very simple and has many practical applications, the image classifier is responsible for assigning a label to the input image from a fixed category group. This article has applied image classification to identify objects by giving the image of the object to be identified, then labeling the image and announcing the label name (object name) through the audio channel. The classification is based on the neural network Inception-v3 model that has been trained on Tensorflow and used Raspberian operating system running on the Raspberry Pi 3 B+ to create a device capable of recognizing objects which compact size and convenient to apply in many fields.
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Muzawi, Rometdo. "Rancang Bangun Prototype Pengontrolan Lampu Gedung STMIK Amik Riau Berbasis IoT Menggunakan Rasberry Pi 3 Model B." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 5, no. 1 (September 24, 2018): 100–108. http://dx.doi.org/10.35957/jatisi.v5i1.127.
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Internet of Things (IoT) is a hardware (Raspberry Pi) that can connect to the internet with the aim to expand the internet network that is connected thoroughly to the hardware. The development of the Internet of things (IoT) has been widely used, especially in this day and age, one of the utilization of this IoT technology is the control of electronic room light equipment through global network controlled via smartphone that can be operated remotely. This research aims to build a remote-control device by utilizing internet technology to perform the process of controlling the lights based on the Internet of Things (IoT). This research is done by building a prototype with mobile based application using python and php programming language. In this research there is a feature of controlling the room lights with the first condition of control of one lamp used to turn one room light and the second condition is used to turn the lights simultaneously.
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Samsinar, Riza, and Sulistiawan Sulistiawan. "Prototype Switching Proyektor Wireless Berbasis Web dengan Virtual Network Computing (Vnc) Server Menggunakan Raspberry Pi 3." RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) 3, no. 2 (December 7, 2020): 71. http://dx.doi.org/10.24853/resistor.3.2.71-74.
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Pada perangkat wireless ini berfungsi untuk memudahkan pengguna menghubungkan komputer atau laptop ke projector. Biasanya pengguna masih menggunakan kabel penghubung diantara komputer atau laptop ke projector. Penggunaan perangkat wireless ini memiliki jangkuan luas untuk pengguna. Penggunaan perangkat wireless ini dapat digunakan pada ruangan terbuka. Jangkauan yang dapat diakses oleh pengguna hingga 6 Meter. Dalam menggunakan perangkat wireless ini pengguna perlu menginstal driver software agar dapat digunakan. Software tersebut berfungsi sebagai perintah agar terkoneksi pada perangkat wireless sesuai yang diharapkan. Jika pengguna ingin menghubungkan ke perangkat wireless harus mempunyai Wi-Fi pada komputer atau laptop. Mini PC Raspberry pi 3 model B+ ini berfungsi sebagai pemproses perintah yang dilakukan oleh pengguna. Penggunaan keseluruhan pengguna mengakses melalui internet pada modem dengan security key didalamnya, kemudian membuka browser untuk mengkoneksikannya. Dalam penggunaan perangkat wireless ini pengguna yang telah terhubung dan diijinkan oleh admin akan tampil pada layar projector. This wireless device serves to make it easier for users to connect a computer or laptop to the projector. Usually users still use a connecting cable between a computer or laptop to the projector. The use of this wireless device has a broad reach for users. The use of this wireless device can be used in open spaces. User-accessible range up to 6 Meters. In using this wireless device the user needs to install the driver software so that it can be used. The software functions as a command to connect to wireless devices as expected. If users want to connect to wireless devices, they must have Wi-Fi on a computer or laptop. This mini PC Raspberry pi 3 model B+ functions as a processing command made by the user. The overall use of the user accesses via the internet on a modem with a security key in it, then opens a browser to connect it. In using this wireless device, users who have been connected and authorized by the admin will appear on the projector screen.
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Llanganate-Peñafiel, Julissa Marlene, Luís Albarracín-Zambrano, and Dionisio Vitalio Ponce-Ruíz. "Sistema automático de alarma sísmica con Raspberry PI para el campus UNIANDES – Quevedo – Ecuador." Revista Arbitrada Interdisciplinaria Koinonía 5, no. 2 (July 21, 2020): 4. http://dx.doi.org/10.35381/r.k.v5i2.851.
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La sociedad del conocimiento tiene como uno de sus fundamentos el aprendizaje desde las TIC, lo cual implica no solo aprender un manejo instrumental, sino, crear, innovar, a través de los medios tecnológicos. La investigación tuvo por objetivo crear un sistema automático de alarma sísmica con Raspberry PI para el campus de la Universidad Regional Autónoma de Los Andes (UNIANDES) – Quevedo de Ecuador, empleando una metodología sistémica, para lo cual se procedió mediante cinco fases investigativas. Se diseñó y desarrolló el sistema automático, para así aplicar su funcionamiento en el campus de UNIANDES - Quevedo, generando así una rápida reacción al alertar un evento sísmico, teniendo en consideración los procedimientos y protocolos de seguridad adoptados por la institución, dedicando el estudio a las tecnologías y bondades que ofrece la tarjeta Raspberry pi 3 modelo B+ en la automatización de procesos.
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Prasath, K. S. "Analysis of Potholes on Road Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1731–34. http://dx.doi.org/10.22214/ijraset.2021.37655.
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Abstract: Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays, image processing is one among rapidly growing technologies. It forms core research area within engineering and computer science disciplines too. Image detection on road is primarily carried out with the help of camera with Raspberry pi 3 model b+ and stimulation software. The device is built in such a way that we can identify any potholes in the respective roads and able to rectify as soon as possible with the help of the device. The data signals shared by the device will be converted to text signals from which we can get it right. These devices are fixed at top of the lamppost which is located at the corners of the road from where the device is monitoring the road at 120 degree for weekly once respectively. Keywords: Image processing, Image detection on road, Raspberry pi 3, 120 degree
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Arifudin, Ahmad. "Rancang Bangun Sistem Keamanan Pintu Rumah Menggunakan Metode Segitiga Wajah (triangle face) Berbasis Raspberry Pi." Jurnal Teknologi Elektro 12, no. 1 (January 31, 2021): 29. http://dx.doi.org/10.22441/jte.2021.v12i1.006.
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Seiring perkembangan teknologi, semakin banyak peralatan-peralatan maupun sistem keamanan canggih berbasis teknologi yang mutakhir. Semakin tingginya angka kriminalitas terutama pencurian dan perampokan mendorong diperlukannya sistem keamanan yang lebih efektif dan efisien. Keamanan pintu rumah yang baik tentu memiliki sistem penguncian yang baik pula, yang kecil kemungkinannya terjadi pembobolan. Pada perancangan sistem keamanan pintu rumah menggunakan metode segitiga wajah (triangle face) berbasis raspberry pi 3 model B+ yang memiliki kelebihan salah satunya mudah,praktis dalam penggunaan untuk dapat meningkatkan kenyamanan dan keamanan dalam membuka pintu rumah tanpa harus memegang bermacam-macam kunci yang mungkin sangat menggangu. Penggunaan fitur Haar Casecade Classifier dengan OpenCV digunakan sebagai pemograman yang berfungsi untuk melakukan deteksi terhadap suatu objek yang pada penelitian ini adalah wajah. Berdasarkan hasil pengujian pada sistem yang telah dirancang, pengenalan dengan metode segitiga wajah memiliki keakurasian 92% di pencahayaan 104 lux dan keakurasian 84% di pencahayaan yang lebih rendah yaitu 53 lux
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Bayat, M., H. Latifi, and A. Hosseininaveh. "THE ARCHITECTURE OF A STEREO IMAGE BASED SYSTEM TO MEASURE TREE GEOMETRIC PARAMETERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 183–89. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-183-2019.
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Abstract. Stereo photogrammetry enables collecting precise and detailed three-dimensional data of terrestrial objects. The estimation of qualitative and quantitative tree attributes, in particular those related to geometric measures, is crucial for forest management. In this study, a stereo imaging system is designed in order to measure a set of geometric attributes of urban trees such as crown dimensions, height and diameter at multiple height levels. The system consists of two hardware and software components. The hardware comprises two cameras with a specified baseline, two raspberry pi 3 model B+ boards, a GPS, an IMU and a power bank, all embedded in a box. The software includes a connection between the camera and the raspberry pi 3 in each side as well as data transfer to a laptop. The calibration is conducted in laboratory prior to applying the system and leads to achieve a disparity image from a pair of stereo imagery, which is then processed to extract dense point clouds. The system enables measuring basic, yet crucial tree attributes such as height and diameter in near real-time basis. The entire process is conducted by means of drastic libraries in Robot Operating System (ROS). Apart from being convenient and real-time, the system is associated with the potential for timely and precise measurements, which enable comparative analysis against other existing remote measurement systems as well as reference field data.
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Iglehart, Brian. "MVO Automation Platform: Addressing Unmet Needs in Clinical Laboratories with Microcontrollers, 3D Printing, and Open-Source Hardware/Software." SLAS TECHNOLOGY: Translating Life Sciences Innovation 23, no. 5 (May 10, 2018): 423–31. http://dx.doi.org/10.1177/2472630318773693.
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Laboratory automation improves test reproducibility, which is vital to patient care in clinical laboratories. Many small and specialty laboratories are excluded from the benefits of automation due to low sample number, cost, space, and/or lack of automation expertise. The Minimum Viable Option (MVO) automation platform was developed to address these hurdles and fulfill an unmet need. Consumer 3D printing enabled rapid iterative prototyping to allow for a variety of instrumentation and assay setups and procedures. Three MVO versions have been produced. MVOv1.1 successfully performed part of a clinical assay, and results were comparable to those of commercial automation. Raspberry Pi 3 Model B (RPI3) single-board computers with Sense Hardware Attached on Top (HAT) and Raspberry Pi Camera Module V2 hardware were remotely accessed and evaluated for their suitability to qualify the latest MVOv1.2 platform. Sense HAT temperature, barometric pressure, and relative humidity sensors were stable in climate-controlled environments and are useful in identifying appropriate laboratory spaces for automation placement. The RPI3 with camera plus digital dial indicator logged axis travel experiments. RPI3 with camera and Sense HAT as a light source showed promise when used for photometric dispensing tests. Individual well standard curves were necessary for well-to-well light and path length compensations.
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Muangma, Rakdiaw, Kanitta Supawan, Meechai Thepnurat, Parinya Saphet, and Anusorn Tong-on. "Development of DAS for prototype of brinell-macro-hardness tester using triplex of force-resistive-sensors manipulated by raspberry pi 3 model B." Journal of Physics: Conference Series 1380 (November 2019): 012086. http://dx.doi.org/10.1088/1742-6596/1380/1/012086.
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Villaseñor-Aguilar, Marcos J., J. Enrique Botello-Álvarez, F. Javier Pérez-Pinal, Miroslava Cano-Lara, M. Fabiola León-Galván, Micael-G. Bravo-Sánchez, and Alejandro I. Barranco-Gutierrez. "Fuzzy Classification of the Maturity of the Tomato Using a Vision System." Journal of Sensors 2019 (July 4, 2019): 1–12. http://dx.doi.org/10.1155/2019/3175848.
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Artificial vision systems (AVS) have become very important in precision agriculture applied to produce high-quality and low-cost foods with high functional characteristics generated through environmental care practices. This article reported the design and implementation of a new fuzzy classification architecture based on the RGB color model with descriptors. Three inputs were used that are associated with the average value of the color components of four views of the tomato; the number of triangular membership functions associated with the components R and B were three and four for the case of component G. The amount of tomato samples used in training were forty and twenty for testing; the training was done using the Matlab© ANFISEDIT. The tomato samples were divided into six categories according to the US Department of Agriculture (USDA). This study focused on optimizing the descriptors of the color space to achieve high precision in the prediction results of the final classification task with an error of 536,995×10-6. The Computer Vision System (CVS) is integrated by an image isolation system with lighting; the image capture system uses a Raspberry Pi 3 and Camera Module Raspberry Pi 2 at a fixed distance and a black background. In the implementation of the CVS, three different color description methods for tomato classification were analyzed and their respective diffuse systems were also designed, two of them using the descriptors described in the literature.
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Wu, Yu Tzu, Matheus K. Gomes, Willian HA da Silva, Pedro M. Lazari, and Eric Fujiwara. "Integrated Optical Fiber Force Myography Sensor as Pervasive Predictor of Hand Postures." Biomedical Engineering and Computational Biology 11 (January 2020): 117959722091282. http://dx.doi.org/10.1177/1179597220912825.
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Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.
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Wati Mohamad Yusof, Yuslinda, Muhammad Asyraf Mohd Nasir, Kama Azura Othman, Saiful Izwan Suliman, Shahrani Shahbudin, and Roslina Mohamad. "Real-Time Internet Based Attendance Using Face Recognition System." International Journal of Engineering & Technology 7, no. 3.15 (August 13, 2018): 174. http://dx.doi.org/10.14419/ijet.v7i3.15.17524.
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This project focuses on face recognition implementation in creating fully automated attendance system with a cloud. Cloud services will provide a useful information regarding the attendance such as attendance summary performance and visualizing the data into graph and chart. In this study, we aim to create an online student attendance database, interfaced with a face recognition system based on raspberry pi 3 model B. A graphical user interface (GUI) will provide ease of use for data analysis on the attendance system. This work used open computer vision library and python for face recognition system combined with SFTP to establish connection to an internet server which runs on PHP and Node.js. The results showed that by interfacing a face recognition system with a server, a real-time attendance system can be built and be monitored remotely.
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Eridani, Dania, Eko Didik Widianto, and Nur Kholid. "Rancang Bangun Sistem Monitoring Dan Controlling Tambak Udang Windu Dengan Konsep Internet Of Things Menggunakan Protokol Message Queuing Telemetry Transport." CESS (Journal of Computer Engineering, System and Science) 5, no. 1 (January 31, 2020): 137. http://dx.doi.org/10.24114/cess.v5i1.14718.
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Udang windu merupakan salah satu jenis udang asli dari Indonesia. Pembudidayaan udang windu sangat dipengaruhi oleh kualitas air pada tambak udang windu. Berdasarkan faktor tersebut, maka dibuatlah sistem yang mampu memantau dan mengontrol kualitas air pada tambak udang windu secara kontinyu dan real-time menggunakan konsep Internet of Things dengan protokol Message Queuing Telemetry Transport. Sistem yang dibangun terdiri dari 2 bagian, yang pertama adalah NodeMCU sebagai primary node yang terhubung dengan sensor (HC-SR04, SEN0161, dan DS18B20) untuk pemantauan kualitas air dan aktuator (motor DC sebagai kincir air). Bagian kedua adalah Raspberry Pi 3 Model B sebagai MQTT broker dan berfungsi untuk mengirimkan hasil pembacaan sensor menuju database. Hasil dari penelitian ini adalah sistem dapat memantau kualitas air dan juga melakukan kontrol terhadap kincir air melalui aplikasi berbasis website. Primary node juga bisa berkomunikasi dengan broker melalui protokol MQTT.
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Kanani, Pratik, and Mamta Padole. "Implementing and Evaluating Health as a Service in Fog and Cloud Computing using Raspberry Pi." International Journal of Intelligent Engineering and Systems 13, no. 6 (December 31, 2020): 142–55. http://dx.doi.org/10.22266/ijies2020.1231.13.
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Internet of Things (IoT) generates a myriad amount of data, which is sent over the Cloud computing infrastructure for analytics and Business Intelligence. This application scenario suffers network delays, transmission delays and delays in decision making. Due to these drawbacks, the Cloud-based IoT infrastructure is not suitable for time-critical health care applications. To overcome this problem, a smart way is introduced called “Fog Computing” - a LAN based processing approach which has multiple advantages. When IoT, Fog and Cloud Computing are combined, the resultant system’s performance is far better. Hence, the combination results in a very efficient Health Care system. Fog and Cloud Computing have their dimensions that not only support each other but also explore many new application domains. In this paper, the real-time ElectroCardioGram (ECG) based Health Care system is implemented in Cloud and Fog Computing. Different Quality of Service (QoS) parameters like memory consumption, transmission delays, computation delays, network delays, Carbon dioxide emission, data transferred and response time are measured, analyzed and improved to make the system more efficient. Based on the Fog computing characteristics and capabilities, the Raspberry Pi 3 B+ model is configured as a Health Care serving gateway by using different installation and configuration steps. Initially, the proposed system is tested for one patients ECG data analysis over cloud and Fog. In every set up all QoS parameters are measured and later the system is subjected to multiple ECG streams for varying numbers of patients to find the limitations of the Raspberry Pi node as a Fog Computing node. The obtained results show that for more number of ECG streams the Fog node is not able maintain QoS in decision making time. Every QoS parameter is explored in detail for decision-making time. In the end, the Fog computing based proposed system is concluded for its pros and cons and future aspects of the Fog node are discussed to make better systems.
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Sokolov, Volodymyr, Bohdan Vovkotrub, and Yevhen Zotkin. "COMPARATIVE BANDWIDTH ANALYSIS OF LOWPOWER WIRELESS IOT-SWITCHES." Cybersecurity: Education, Science, Technique, no. 5 (2019): 16–30. http://dx.doi.org/10.28925/2663-4023.2019.5.1630.
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The article presents the research and comparative analysis of the bandwidth of low-power wireless IoT devices as wireless switches. The following IoT devices were investigated: Raspberry Pi 3 Model B and Raspberry Pi Zero W. The DS18B20 and INA219 sensors investigated and analyzed the dependence of FTP multimedia data transmission speed on wireless Wi-Fi network on the temperature of the switch processor, temperature. The environment and the current and voltage consumed by the switch. Advantages of sensors with GPIO interface over analog meters for this experiment are revealed. Much of the work is devoted to the development of automation of results from GPIO interfaces, which helped eliminate human error and get more accurate metrics. Measurement automation was developed using Python 3.7 programming language. Using the INA219 library we were able to obtain current and voltage indicators from the ina219 board. To get temperature indicators sufficiently built into Python libraries to read temperature files in Raspbian. The article focuses on the synchronicity of measurement results records for more accurate analysis. Therefore, an FTP client was developed that measures the download speed of the file from the FTP server and records the results simultaneously with temperature, current and voltage measurements. To this end, attention is drawn to the multithreading in Python programming language and the transmission of commands using TCP sockets in that language. As a result, the dependence of the measured factors was calculated using the Pearson correlation formula. These measurement factors affect the autonomy and energy consumption, which is very important for IoT devices, and therefore, among the devices tested, recommendations were made regarding their choice when used depending on the conditions.
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Rudiansyah, Hendy, Gun Gun Maulana, and Atep Puja T H. "Pengendalian Robot Humanoid Menggunakan Metode Speech Recognation Berbasis Android." Jurnal Teknologi dan Rekayasa Manufaktur 2, no. 1 (May 8, 2020): 15–30. http://dx.doi.org/10.48182/jtrm.v2i1.14.
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Pengendalian Robot saat ini memerlukan sistem kontrol yang dapat memudahkan interaksi antara manusia dan robot sehingga dalam hal ini dibutuhkan sebuah sistem pengendalian dengan perangkat yang banyak digunakan dalam kehidupan sehari-hari yaitu Smartphone dengan Sistem Operasi Android. Penelitian ini bertujuan untuk merancang dan membuat sistem pengendalian robot humanoid dengan memanfaatkan smartphone android sebagai media komunikasi dan intruksi dengan menggunakan perintah suara. pada Android serta sebagai data perintah pergerakan robot humanoid yang ditransmisikan secara online melalui Firebase yang berarti Aplikasi Smartphone dan Robot harus terkoneksi internet. Perangkat pengolah data yang digunakan pada robot adalah Raspberry Pi 3 Model B dengan protokol komunikasi serial dalam mengirim data pergerakan ke motor servo. Hasil penelitian menunjukan bahwa robot dapat dikendalikan dengan perintah suara secara jarak jauh pada Smartphone, serta mudah dioperasikan tanpa membutuhkan perangkat khusus, dari hasil uji kecepatan pengiriman data yaitu rata-rata 5,58 detik dengan kecepatan internet sebesar 7,79Mbps, kemudian penangkapan suara rata-rata error sebesar 1,86%.
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Monita, Monita, and Hendri Hendri. "Sistem Kontrol Rumah Pintar Menggunakan Kamera Berbasis IoT." JTEIN: Jurnal Teknik Elektro Indonesia 2, no. 1 (May 23, 2021): 107–12. http://dx.doi.org/10.24036/jtein.v2i1.141.
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Tingginya angka kriminalitas di Indonesia merupakan salah satu akibat dari krisis ekonomi. Tentunya tindakan kejahatan semakin banyak dilakukan, terutama dalam hal pencurian dan perampokan. Pemaparan dari hasil tugas akhir yang bertujuan untuk merancang sistem kontrol rumah pintar menggunakan kamera dengan type kamera logitech C270 HD web cam serta raspberry pi 3 model B sebagai pusat pengontrolan, sensor PIR berfungsi untuk mengirimkan notifikasi terhadap klien ketika mendeteksi adanya gerakan, serta smartphone sebagai penerima data mp4 jika ada data diproses melalui aplikasi telegram. Pada tugas akhir ini, penulis menggunakan metode penelitian kuantitatif. Dimana pengujian dilakukan menggunakan data berupa angka untuk menganalisis keterangan yang ingin diketahui sehingga terlihat lebih detail dan jelas. Untuk memudahkan pembacaan, penulis menggunakan tabel-tabel sebagai penjabaran hasil pengujian. Setelah pengujian, kamera dipasang di prototype dan di teras rumah sehingga klien bisa menerima video jika ada aktifias di sekitar teras rumah. Klien bisa mendeteksi dari jarak jauh melalui aplikasi Telegram di smartphone. Data yang diambil menunjukkan bahwa fungsi keseluruhan cukup baik dan membutuhkan waktu 3-18 detik durasi video.
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Suryatini, Fitria, Maimunah Maimunah, and Fachri Ilman Fauzandi. "Implementasi Sistem Kontrol Irigasi Tetes Menggunakan Konsep IoT Berbasis Logika Fuzzy Takagi-Sugeno." JTERA (Jurnal Teknologi Rekayasa) 4, no. 1 (June 19, 2019): 115. http://dx.doi.org/10.31544/jtera.v4.i1.2019.115-124.
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Irigasi merupakan faktor penting dalam proses budidaya tanaman. Oleh karena itu, dibutuhkan upaya pengelolaan air secara tepat khususnya dalam irigasi. Salah satu metode irigasi yang banyak digunakan adalah irigasi tetes. Penelitian ini bertujuan untuk merancang sistem kontrol irigasi tetes berdasarkan kondisi suhu dan kelembapan tanah menggunakan kendali logika fuzzy Takagi-Sugeno yang diimplementasikan menggunakan konsep Internet of Things (IoT). Perangkat keras yang digunakan adalah Raspberry Pi 3 model B sebagai pusat kendali, sensor suhu DS18B20, dan sensor kelembapan tanah SKU:SEN0193. Keluaran kendali fuzzy menentukan durasi penyalaan solenoid valve untuk mengairi tanaman. Sumber air irigasi berasal dari tangki yang dapat terisi secara otomatis menggunakan motor pompa dan sensor ultrasonik HCSR04 sebagai pendeteksi level air. Aplikasi Android digunaka untuk kendali jarak jauh dan monitoring parameter yang dikirim secara realtime melalui database online Firebase. Hasil penelitian menunjukkan bahwa sistem dapat menjaga kelembapan tanah pada kelembapan rata-rata sebesar 98,4% dengan durasi penyiraman rata-rata sebesar 453,6 detik. Rata-rata volume air yang terpakai pada proses penyiraman sebanyak 10,9 liter. Selain itu, sistem dapat melakukan proses monitoring dan pengontrolan jarak jauh dengan delay rata-rata 2 detik.
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Yen, Chih-Ta, Sheng-Nan Chang, and Cheng-Yang Cai. "Development of a Continuous Blood Pressure Measurement and Cardiovascular Multi-Indicator Platform for Asian Populations by Using a Back Propagation Neural Network and Dual Photoplethysmography Sensor Signal Acquisition Technology." Journal of Nanomaterials 2021 (May 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/6613817.
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This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a back propagation neural network (BPNN) that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators. The proposed platform measured the signal changes in PPG and converted them into physiological indicators, such as pulse transit time (PTT), pulse wave velocity (PWV), perfusion index (PI), heart rate (HR), and pulse wave analysis (PWA); these indicators were then fed into the BPNN to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-to-digital converter [MCP3008]), which were connected using a serial peripheral interface. The BPNN algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure (SBP) and diastolic blood pressure (DBP). To increase the robustness of the BPNN model, this study input data of 100 Asian participants into the training database, including those with and without cardiovascular disease, each with a proportion of approximately 50%. The experimental results revealed that the mean and standard deviation of SBP were 2.23 ± 2.24 mmHg , with a mean squared error of 3.15 mmHg. The mean and standard deviation of DBP was 3.5 ± 3.53 mmHg , with a mean squared error of 4.96 mmHg. The proposed real-time blood pressure measurement system exhibited a mean accuracy of 98.22% and 95.58% for SBP and DBP, respectively.
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Elngar, Ahmed A., and Mohammed Kayed. "Vehicle Security Systems using Face Recognition based on Internet of Things." Open Computer Science 10, no. 1 (March 20, 2020): 17–29. http://dx.doi.org/10.1515/comp-2020-0003.
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AbstractNowadays, the automobile sector is one of the hottest applications, where vehicles can be intelligent by using IoT technology. But unfortunately, these vehicles suffer from many crimes. Hence it has become a big challenge for the IoT to avoid such these crimes from professional thieves. This paper presents a proposal for the development of a vehicle guard and alarm system using biometric authentication based on IoT technology. Whereas, for vehicle security issues; the proposed system VSS − IoT gives only full access for authorized vehicle’s driver based on the interface of a Raspberry Pi 3 Model B+ development board, Pi camera, PIR sensor, and smart-phone. Therefore, if the proposed system detects an unauthorized person inside the vehicle, then the system will notify and send his image to vehicle’s owner and/or to a police workstation through the Internet, as well as, its location in case the vehicle is stolen or damaged. The proposed system is tested on two datasets that are ORL dataset and our dataset. The experimental results of the VSS − IoT showed that the accuracy is 98.2% on ORL dataset, whereas 99.6% when applied on our dataset. Besides, the VSS − IoT enhances the sensitivity to 97.7% which is important for real-time. As well as the result demonstrated that the proposed system took shorter time 0.152 sec under different illumination conditions, when the value of the threshold is 3 * 103 and 3.50 * 103. Therefore, the VSS − IoT is very robust and reliable for face recognition when deployed on the low-power processor.
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Syukur, Arba Abdul. "Implementasi Webcam sebagai Pendeteksi Wajah pada Sistem Keamanan Perumahan menggunakan Image Processing." ELECTRICES 2, no. 1 (May 22, 2020): 1–5. http://dx.doi.org/10.32722/ees.v2i1.2791.
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Pencurian yang sangat meresahkan masyarakat seringkali terjadi pada suatu ruangan atau lingkungan seperti gedung, kantor, lorong bahkan tempat ibadah juga menjadi sasaran para pencuri. Upaya yang dilakukan DKM (Dewan Kemakmuran Masjid) yaitu memberikan himbauan supaya tetap menjaga barang pentingnya masing-masing. Masjid seharusnya menjadi tempat yang aman dan nyaman untuk dikunjungi. Oleh karena itu kami memiliki ide yang bertujuan untuk mengantisipasi pencurian di masjid atau tempattempat yang rawan pencurian. Penelitian ini merancangbangun sistem pengenalan wajah sebagai solusi untuk mengurangi tingkat pencurian. Sistem ini dilengkapi dengan perangkat keras Raspberry Pi 3 model B dan webcam A4Tech. Perangkat lunak database yang dapat menyimpan data pengguna. Tujuan penelitian untuk membandingkan 2 metode yang terbaik dalam pengenalan wajah yaitu metode LBPH (Local Binary Pattern Histogram) dan metode Eigenface. Penelitian dilakukan pada siang hari untuk mengambil citra wajah yang berbeda. Penelitian dilakukan dengan 3 kondisi yaitu siang hari luar ruangan, siang hari dalam ruangan dan malam hari dalam ruangan. Parameter yang digunakan untuk melihat hasil dari pengenalan wajah yaitu Akurasi, FAR (False Accept Rate) dan FRR (False Reject Rate). Hasil pengujian 2 metode tersebut yang memiliki tingkat rata-rata Akurasi tertinggi dan tingkat rata-rata FAR dan FRR terendah adalah metode Eigenface. Kesimpulan dari hasil penelitian yaitu pencahayaan mempengaruhi pengenalan wajah dalam 2 metode tersebut.
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Maulana, Kresna Lita, Achmad Hidayatno, and Imam Santoso. "APLIKASI PENGENALAN WAJAH MENGGUNAKAN METODE EIGENFACE DAN JARAK EUCLIDEAN." TRANSIENT 7, no. 1 (March 12, 2018): 62. http://dx.doi.org/10.14710/transient.7.1.62-69.
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Wajah merupakan salah satu identitas bagi setiap individu pada sistem biometrik. Wajah merupakan ciri unik dari setiap manusia yang dapat membedakan rupa antar manusia. Berbeda dengan manusia yang dapat mengenali wajah dengan mudah dan cepat, komputer tidak secepat dan semudah manusia. Pada komputer diperlukan suatu algoritme dalam pengenalan wajah. Pada Penelitian ini, dirancang suatu sistem pengenalan wajah menggunakan kamera web dan OpenCV yang terpasang pada Raspberry Pi 3 Model B. Masukan sistem berupa video real-time yang diperoleh dari kamera web. Metode yang digunakan pada pendeteksian wajah adalah metode Viola-Jones dan dalam pengenalan wajah digunakan metode eigenface dan jarak euclidean. Terdapat 5 responden yang diambil citra wajahnya sebagai database. Hasil yang diperoleh dari sistem ini adalah nama dari setiap responden yang terdapat pada database. Berdasarkan hasil pengujian pada kondisi dalam ruangan dihasilkan rata-rata akurasi sebesar 99,8%, sedangkan pada kondisi luar ruangan dihasilkan rata-rata akurasi sebesar 93,8%. Pada pengujian citra wajah yang diberi derau salt & pepper dengan kepadatan derau 0,001 dan 0,01 didapatkan bahwa program mampu mengenali wajah dengan benar. Program mampu mengenali wajah dengan benar pada citra yang dirotasi sebesar 10 derajat.
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Jung, Sunghun. "Development and Verification of Hybrid Power Controller Using Indoor HIL Test for the Solar UAV." Energies 13, no. 8 (April 24, 2020): 2110. http://dx.doi.org/10.3390/en13082110.
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A hybrid power system (HPS) is developed for the photovoltaic (PV) powered and tethered multirotor unmanned aerial vehicle (UAV) based on the robot operating system (ROS) and verified using an indoor hardware-in-the-loop (HIL) test. All the processes, including a UAV flight mode change (i.e., takeoff, hovering, and landing) and power flow control (consisting of PV modules, a LiPo battery pack, and a UAV) are completely automated using a combination of Pixhawk 2.1 and the Raspberry Pi 3 Model B (RPi 3B). Once the indoor HIL test starts, (1) the UAV takes off and hovers with a preassigned 10 m altitude at a fixed point and keeps hovering until the voltage drops below 13.4 V ; (2) the UAV lands when the voltage drops below 13.4 V, and the hybrid power controller (HPC) starts to charge the LiPo battery pack using the energy from PV modules; and (3) the UAV takes off when the voltage of the battery pack becomes more than 16.8 V, and the procedure repeats from (1). A PV-powered and tethered multirotor UAV using the proposed HPS can fly more safely for a longer time, particularly in an urban area, and so it is competitive to the traditional multirotor type UAV in the sense of both the flight time and the surveillance mission performance.
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Ingole, Mr Shubham. "Vehicle Vacant Seat Identification and Mask Detection using Image processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 10, 2021): 118–21. http://dx.doi.org/10.22214/ijraset.2021.36253.
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This article describes the technique of real-time face detection, mask detection, and vacant seat available in the vehicle. There are so many technologies for finding seat availability in the vehicle. But image processing technology is very popular today. Face detection is part of image processing. It is used to find the face of a human being in a certain area. Face detection is used in many applications, such as facial recognition, people tracking or photography. In this paper, the face detection technique is used to detect the vacant seat availability in the vehicle and also to detect whether the passenger wear the mask on his face or not. The webcam is installed in the vehicle and connected with the Raspberry Pi 3 model B. When the vehicle leaves the station, the webcam will capture images of the passengers in the seating area. The webcam will be mounted on the vehicle. The images will be adjusted and enhanced to reduce noise made by the software application. The system obtains the maximum number of passengers in the vehicle that processes the images and then calculates the availability of seats in the vehicle. In covid-19 situation mask detection is necessary. so this system also used to detect the mask on face.
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Fadillah, Rahmat, and Legiman Slamet. "PERANCANGAN APLIKASI MOBILE LEARNING BERBASIS ANDROID DI SMK NEGERI 6 PADANG." Voteteknika (Vocational Teknik Elektronika dan Informatika) 7, no. 2 (June 1, 2019): 61. http://dx.doi.org/10.24036/voteteknika.v7i2.104197.
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Mobile Learning was a learning approach that implicates the mobile devices like smartphone, PDA, tablet PC which the learner was able to access the material, instruction, and application that were relevant to the lesson without being constrained by space and time wherever or whenever they were. Mobile Learning was one of alternative for problem solving in education which comprises the problem of distribution access for education content, content quality, and others. Then, in order to strengthen the source of information for the user and minimized the cost toward the access of that education content. The new inovation for system of study that based on application in systematically and structured as the interactive media for student of learning process in SMK Negeri 6 Padang. This application could be the appropriate solution for learning in the school and minimize the incorrectness. The design of this system was implementationed by using language of program PHP 7.3.0 with MYSQL of database. In order to design this system involved Used Case Diagram, Activity Diagram, Context Diagram, Flowmap, Normalization, and Entity Relationship Diagram. This system was involved at least 3 users that are Administrator, Teacher, and Student. The registered users have right to access for the system by login with using username and password. This application was designed by using mini server Raspberry Pi 3 model B+ on the web server which based on Moodle, and platform android as the application for client.Keywords: Mobile Learning, Android, PHP, MYSQL, Moodle.
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Hong, Cheol-Ho, Kyungwoon Lee, Minkoo Kang, and Chuck Yoo. "qCon: QoS-Aware Network Resource Management for Fog Computing." Sensors 18, no. 10 (October 13, 2018): 3444. http://dx.doi.org/10.3390/s18103444.
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Fog computing is a new computing paradigm that employs computation and network resources at the edge of a network to build small clouds, which perform as small data centers. In fog computing, lightweight virtualization (e.g., containers) has been widely used to achieve low overhead for performance-limited fog devices such as WiFi access points (APs) and set-top boxes. Unfortunately, containers have a weakness in the control of network bandwidth for outbound traffic, which poses a challenge to fog computing. Existing solutions for containers fail to achieve desirable network bandwidth control, which causes bandwidth-sensitive applications to suffer unacceptable network performance. In this paper, we propose qCon, which is a QoS-aware network resource management framework for containers to limit the rate of outbound traffic in fog computing. qCon aims to provide both proportional share scheduling and bandwidth shaping to satisfy various performance demands from containers while implementing a lightweight framework. For this purpose, qCon supports the following three scheduling policies that can be applied to containers simultaneously: proportional share scheduling, minimum bandwidth reservation, and maximum bandwidth limitation. For a lightweight implementation, qCon develops its own scheduling framework on the Linux bridge by interposing qCon’s scheduling interface on the frame processing function of the bridge. To show qCon’s effectiveness in a real fog computing environment, we implement qCon in a Docker container infrastructure on a performance-limited fog device—a Raspberry Pi 3 Model B board.
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Gupta, Maansi. "Raspberry Pi(3 b) based Smart Door." International Journal for Research in Applied Science and Engineering Technology 7, no. 4 (April 30, 2019): 3333–36. http://dx.doi.org/10.22214/ijraset.2019.4559.
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Chernenko, Roman, Olena Riabchun, Maksym Vorokhob, Andriy Anosov, and Valerii Kozachok. "INCREASING THE LEVEL OF SECURITY OF INTERNET THINGS NETWORK SYSTEMS DUE TO ENCRYPTION OF DATA ON DEVICES WITH LIMITED COMPUTER SYSTEMS." Cybersecurity: Education, Science, Technique 3, no. 11 (2021): 124–35. http://dx.doi.org/10.28925/2663-4023.2021.11.124135.
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Because IoT devices work with data that may be confidential or confidential, that data must be protected. Due to the peculiarities of platforms and the implementation of such systems, namely: first, the use of devices with limited computing characteristics, which makes it impossible to use traditional means of information protection and data transmission protocols, and secondly. systems, and provide them with sufficient computing resources due to the impossibility of laying power lines, thirdly, the lack of standards for the implementation of these devices in the existing infrastructure, there are serious threats to the confidentiality, integrity and availability of information. The article considers the model of the IoT system, oneM2M standard presented by the European Institute of Communication Standards. IoT devices are designed with the necessary network connectivity, but often do not provide reliable network security. Network security is a critical factor in the deployment of IoT devices. The situation is complicated by the fact that IoT largely consists of limited devices. A limited device usually has a very limited cycle of power, memory, and processing. IoT devices are particularly vulnerable to threats because many of the current IoT devices do not support encryption. Several known encryption algorithms were selected for analysis: RSA, Vernam cipher, El Gamal scheme. After analyzing the above algorithms, a prototype of the IoT system was developed using limited devices, which provides absolute cryptographic stability. The prototype consists of a gateway in the role of a Raspberry pi 3 B + microcomputer, a limited Arduino Nano device with a connected sensor and a software implementation of the above-mentioned Vernam cipher with all the tasks.
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Bylykbashi, Kevin, Evjola Spaho, Ryoichiro Obukata, Kosuke Ozera, Yi Liu, and Leonard Barolli. "Implementation and evaluation of an ambient intelligence testbed." International Journal of Web Information Systems 14, no. 1 (April 16, 2018): 123–35. http://dx.doi.org/10.1108/ijwis-12-2017-0082.
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Purpose The purpose of this work is to implement an ambient intelligence (AmI) testbed to improve human sleeping conditions. Design/methodology/approach The implemented testbed is composed of the sensor node, sink node and actor node. As sensor node, the authors use a microwave sensor module (MSM) called DC6M4JN3000, which emits microwaves in the direction of a human or animal subject. These microwaves reflect back off the surface of the subject and change slightly in accordance with movements of the subject’s heart and lungs. As sink node, the authors use Raspberry Pi 3 Model B computers. In the sink node, the data are processed and then clustered by the k-means clustering algorithm. Then, the result is sent to the actor node (Reidan Shiki PAD module), which can be used for cooling and heating the bed. Findings The authors carried out simulations and experiments. Based on the simulation results, it was found that the room lighting, humidity and temperature have different effects on humans during sleeping. The best performance is shown when LIG parameter is 10 units, HUM parameter is 50 and TEM parameter is 25. Based on experimental results, it was found that the implemented AmI testbed has a good effect on humans during sleeping. Research limitations/implications For simulations, three input parameters were considered. However, new parameters that affect human sleeping conditions also need to be investigated. Further, the experiments were carried out for one person. More extensive experiments with multiple people are needed to have a better evaluation. Originality/value In this research work, a new fuzzy-based system was implemented to improve human sleeping conditions. The authors presented three new input parameters to evaluate the output (sleeping condition). The authors implemented and evaluated a testbed and showed that the implemented AmI testbed has a good effect on humans during sleeping, thus improving their quality of life (QoL).
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Jagadish, B., P. K. Mishra, M. P. R. S. Kiran, and P. Rajalakshmi. "A Real-Time Health 4.0 Framework with Novel Feature Extraction and Classification for Brain-Controlled IoT-Enabled Environments." Neural Computation 31, no. 10 (October 2019): 1915–44. http://dx.doi.org/10.1162/neco_a_01223.
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In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled environments (BCE), which uses the classified MI task to assist disabled persons in controlling IoT-enabled environments such as lighting and heating, ventilation, and air-conditioning (HVAC). The first method for classification involves a simple and low-complex classification framework using a combination of regularized Riemannian mean (RRM) and linear SVM. Although this method performs better compared to state-of-the-art techniques, it still suffers from a nonnegligible misclassification rate. Hence, to overcome this, the second method offers a persistent decision engine (PDE) for the MI classification, which improves classification accuracy (CA) significantly. The proposed methods are validated using an in-house recorded four-class MI data set (data set I, collected over 14 subjects), and a four-class MI data set 2a of BCI competition IV (data set II, collected over 9 subjects). The proposed RRM architecture obtained average CAs of 74.30% and 67.60% when validated using datasets I and II, respectively. When analyzed along with the proposed PDE classification framework, an average CA of 92.25% on 12 subjects of data set I and 82.54% on 7 subjects of data set II is obtained. The results show that the PDE algorithm is more reliable for the classification of four-class MI and is also feasible for BCE applications. The proposed low-complex BCE architecture is implemented in real time using Raspberry Pi 3 Model B+ along with the Virgo EEG data acquisition system. The hardware implementation results show that the proposed system architecture is well suited for body-wearable devices in the scenario of Health 4.0. We strongly feel that this study can aid in driving the future scope of BCE research.
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Novrianda Dasmen, Rahmat, and Rasmila . "Implementasi Raspberry Pi 3 pada Sistem Pengontrol Lampu berbasis Raspbian Jessie." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 5, no. 1 (April 23, 2019): 46. http://dx.doi.org/10.26418/jp.v5i1.29720.
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Pada penelitian sebelumnya telah dihasilkan sistem pengontrol lampu menggunakan SMS gateway dengan bantuan perangkat mikrokontroler. Hal tersebut memberikan kemudahan manusia terhadap pengontrolan on/off lampu rumah, sehingga tidak perlu lagi repot untuk menekan saklar lampu yang berada di dinding rumah. Seiring dengan perkembangan teknologi, telah berkembang juga perangkat pengontrol, yaitu Raspberry Pi 3 yang juga sering disebut sebagai mini Personal Computer (PC). Pada penelitian ini digunakan perangkat Raspberry Pi 3 untuk menerapkan Sistem Operasi Raspbian Jessie pada sistem pengontrol lampu serta menggunakan metode action research dalam memperoleh hasil penelitian sesuai dengan tujuan. Selain itu, dibutuhkan juga bahasa pemrograman phyton untuk dapat menjalankan user interface sistem pengontrol lampu berbasis Raspberry Pi 3. Pada pengujian sistem pengontrol lampu, digunakan lampu pijar senter dengan model fitting E10 dan lampu pijar rumah dengan model fitting E27. Hasil penelitian ini menunjukan bahwa Raspberry Pi 3 dengan Raspbian Jessie dibantu dengan perangkat modul relay dapat digunakan untuk mengontrol on/off lampu pijar rumah (model fitting E27) dengan mudah dan baik menggunakan user interface berbasis web.
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Khort, Dmitriy O., Aleksei I. Kutyrev, Igor G. Smirnov, Rostislav A. Filippov, and Roman V. Vershinin. "Developing Algorithms for a Berry Recognition System Used in Robotized Harvesting of Garden Strawberry." Elektrotekhnologii i elektrooborudovanie v APK 67, no. 1 (March 28, 2020): 133–41. http://dx.doi.org/10.22314/2658-4859-2020-67-1-133-141.
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Technological capabilities of agricultural units cannot be optimally used without extensive automation of production processes and the use of advanced computer control systems. (Research purpose) To develop an algorithm for recognizing the coordinates of the location and ripeness of garden strawberries in different lighting conditions and describe the technological process of its harvesting in field conditions using a robotic actuator mounted on a self-propelled platform. (Materials and methods) The authors have developed a self-propelled platform with an automatic actuator for harvesting garden strawberry, which includes an actuator with six degrees of freedom, a co-axial gripper, mg966r servos, a PCA9685 controller, a Logitech HD C270 computer vision camera, a single-board Raspberry Pi 3 Model B+ computer, VL53L0X laser sensors, a SZBK07 300W voltage regulator, a Hubsan X4 Pro H109S Li-polymer battery. (Results and discussion) Using the Python programming language 3.7.2, the authors have developed a control algorithm for the automatic actuator, including operations to determine the X and Y coordinates of berries, their degree of maturity, as well as to calculate the distance to berries. It has been found that the effectiveness of detecting berries, their area and boundaries with a camera and the OpenCV library at the illumination of 300 Lux reaches 94.6 percent’s. With an increase in the robotic platform speed to 1.5 kilometre per hour and at the illumination of 300 Lux, the average area of the recognized berries decreased by 9 percent’s to 95.1 square centimeter, at the illumination of 200 Lux, the area of recognized berries decreased by 17.8 percent’s to 88 square centimeter, and at the illumination of 100 Lux, the area of recognized berries decreased by 36.4 percent’s to 76 square centimeter as compared to the real area of berries. (Conclusions) The authors have provided rationale for the technological process and developed an algorithm for harvesting garden strawberry using a robotic actuator mounted on a self-propelled platform. It has been proved that lighting conditions have a significant impact on the determination of the area, boundaries and ripeness of berries using a computer vision camera.
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Ramelan, Agus, Arief Syaichu Rohman, and Allen Kelana. "Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control." MATEC Web of Conferences 198 (2018): 04007. http://dx.doi.org/10.1051/matecconf/201819804007.
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This paper presents an implementation embedded system for position control of Permanent Magnet Synchronous Motor (PMSM). The control system consists of raspberry pi 3 as a microcontroller, ASDA-A2 servo drive, and Delta Servo ECMA type. The software design includes simulation tool and Python included on Raspbian OS. Communication between Raspberry Pi 3 and ASDA-A2 drivers using the ASCII Modbus communication protocol. Raspberry Pi 3 processes the reference data and the actual reading result and calculates the resulting error. The control algorithm used in this research is Model Predictive Control (MPC). As a Linear Quadratic Regulator, MPC aims to design and generate an optimal control signal with the ability to anticipate saturation, receding horizon, future event and take control accordingly In the design of the MPC technique to adjust the speed of the PMSM to take action of reference tracking, performance index optimization is done by adjusting the value of weighting horizon N, Q and R. The simulation and implementation results showed that the PMSM can reach the stability point on each desired setpoint and result in a near-zero steady-state error.